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- W3214870369 abstract "Stochastic dual dynamic programming (SDDP) is a state-of-the-art method for solving multi-stage stochastic optimization, widely used for modeling real-world process optimization tasks. Unfortunately, SDDP has a worst-case complexity that scales exponentially in the number of decision variables, which severely limits applicability to only low dimensional problems. To overcome this limitation, we extend SDDP by introducing a trainable neural model that learns to map problem instances to a piece-wise linear value function within intrinsic low-dimension space, which is architected specifically to interact with a base SDDP solver, so that can accelerate optimization performance on new instances. The proposed Neural Stochastic Dual Dynamic Programming ($nu$-SDDP) continually self-improves by solving successive problems. An empirical investigation demonstrates that $nu$-SDDP can significantly reduce problem solving cost without sacrificing solution quality over competitors such as SDDP and reinforcement learning algorithms, across a range of synthetic and real-world process optimization problems." @default.
- W3214870369 created "2021-12-06" @default.
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- W3214870369 date "2021-12-01" @default.
- W3214870369 modified "2023-09-26" @default.
- W3214870369 title "Neural Stochastic Dual Dynamic Programming" @default.
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- W3214870369 doi "https://doi.org/10.48550/arxiv.2112.00874" @default.
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